【摘要】：近年來(lái),面對(duì)能源危機(jī)、環(huán)境污染這些日益嚴(yán)峻問(wèn)題的威脅,世界各國(guó)都在加緊研發(fā)電動(dòng)汽車。電動(dòng)汽車以其優(yōu)良的節(jié)能環(huán)保無(wú)污染的特點(diǎn)成為未來(lái)汽車產(chǎn)業(yè)的發(fā)展重點(diǎn)。其中,作為電動(dòng)汽車的動(dòng)力源的電池成為制約電動(dòng)汽車發(fā)展的瓶頸。電池管理系統(tǒng)(Battery Management System,BMS)作為全面監(jiān)控和管理電池的關(guān)鍵,通過(guò)充放電均衡檢測(cè)保障電池正常合理工作,能夠有效提高電池循環(huán)使用壽命,避免不合理使用和降低不必要的風(fēng)險(xiǎn)。其中,電池及電池組剩余電量(State of Charge,SOC)和健康狀態(tài)(State of Health,SOH)的在線估算是電池管理系統(tǒng)合理高效運(yùn)行的關(guān)鍵。研究具有較高精度的SOC及SOH估計(jì)算法對(duì)于電池管理系統(tǒng)而言是極其重要的,它能夠?yàn)檠娱L(zhǎng)電池壽命、提高電池利用率等提供有效的支持。本文以鋰離子電池狀態(tài)估算算法為主要研究?jī)?nèi)容,在分析了現(xiàn)有BMS研究水平的基礎(chǔ)上,結(jié)合鋰離子電池本身的特點(diǎn)進(jìn)行鋰離子電池管理算法的研究和實(shí)現(xiàn)。論文首先通過(guò)對(duì)電池模型的研究介紹,選擇二階RC等效電路模型并通過(guò)MATLAB建立了鋰離子電池非線性模型并進(jìn)行了參數(shù)辨識(shí),結(jié)合實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證了所建模型的有效性。然后通過(guò)對(duì)現(xiàn)在幾種常用的電池荷電狀態(tài)(SOC)估算算法的優(yōu)缺點(diǎn)進(jìn)行分析,基于所建立的鋰離子電池二階模型研究了基于擴(kuò)展卡爾曼濾波的鋰離子電池SOC估算算法,采用Simulink對(duì)SOC估算算法進(jìn)行仿真驗(yàn)證。對(duì)于鋰離子電池健康狀態(tài),通過(guò)對(duì)實(shí)驗(yàn)數(shù)據(jù)的深入分析,提出了一種雙脈沖SOH的快速檢測(cè)算法。最后,根據(jù)對(duì)鋰離子電池SOH的估算采用粒子濾波(Particle Filter)算法對(duì)鋰離子電池剩余使用壽命進(jìn)行了預(yù)測(cè)在MATLAB中進(jìn)行代碼實(shí)現(xiàn)和仿真,與實(shí)驗(yàn)數(shù)據(jù)進(jìn)行對(duì)比驗(yàn)證,達(dá)到了較好的預(yù)測(cè)精度。本文通過(guò)對(duì)鋰離子電池剩余電量及健康狀態(tài)估算方法的研究,以及對(duì)電池壽命的預(yù)測(cè)算法的實(shí)現(xiàn),結(jié)合試驗(yàn)數(shù)據(jù)進(jìn)行對(duì)比,達(dá)到了較好的估算精度,為鋰離子動(dòng)力電池在電動(dòng)汽車及儲(chǔ)能系統(tǒng)中的應(yīng)用奠定了技術(shù)基礎(chǔ)。
[Abstract]:In recent years, facing the threat of energy crisis and environmental pollution, every country in the world is speeding up the research and development of electric vehicles. Electric vehicle (EV) has become the focus of automotive industry in the future because of its excellent characteristics of energy saving, environmental protection and no pollution. Among them, as the power source of electric vehicles, battery becomes the bottleneck of the development of electric vehicles. Battery management system (Battery Management System,BMS) is the key to the overall monitoring and management of the battery. The battery cycle life can be effectively improved by the charge / discharge balance detection to ensure the normal and reasonable operation of the battery. Avoid unreasonable use and reduce unnecessary risks. The online estimation of (State of Charge,SOC and (State of Health,SOH is the key to the reasonable and efficient operation of the battery management system. It is very important to study SOC and SOH estimation algorithms with high accuracy for battery management system, which can provide effective support for prolonging battery life and improving battery utilization rate. In this paper, the state estimation algorithm of lithium ion battery is taken as the main research content. Based on the analysis of the existing BMS research level, the research and implementation of the lithium ion battery management algorithm are carried out according to the characteristics of the lithium ion battery itself. The second order RC equivalent circuit model is selected and the nonlinear model of lithium-ion battery is established by MATLAB. The validity of the model is verified by the experimental data. Then the advantages and disadvantages of several commonly used (SOC) estimation algorithms are analyzed. Based on the established second-order model of lithium-ion battery, the SOC estimation algorithm based on extended Kalman filter is studied. The SOC estimation algorithm is simulated by Simulink. For the healthy state of lithium ion battery, a fast detection algorithm of double pulse SOH is proposed by analyzing the experimental data. Finally, according to the estimation of lithium ion battery SOH, the residual service life of lithium ion battery is predicted by particle filter (Particle Filter) algorithm. The code realization and simulation are carried out in MATLAB, and the results are compared with the experimental data. Good prediction accuracy has been achieved. In this paper, the method of estimating the residual charge and health state of Li-ion battery is studied, and the prediction algorithm of battery life is realized. In combination with the experimental data, the estimation accuracy is achieved. It lays a technical foundation for the application of lithium ion power battery in electric vehicle and energy storage system.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM912

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本文編號(hào)：2334157